Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity

• URL: http://arxiv.org/abs/2606.05532v1
• Date: Thu, 04 Jun 2026 00:21:35 GMT
• Title: Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity
• Abstract summary: Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity.<n>We propose selective metacognitive adaptation: routine AI use redistributes rather than uniformly diminishes metacognitive effort.<n>We present a taxonomy of six metacognitive capacities organized by temporal phase, characterize their tendencies under routine AI use, and show how individually rational adaptation produces emergent social costs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity. Current explanations -- cognitive offloading and over-reliance -- identify symptoms but not mechanisms. We propose selective metacognitive adaptation: routine AI use redistributes rather than uniformly diminishes metacognitive effort. Some capacities are amplified (partner modeling, surface control), while others are systematically under-supported (originality evaluation, reflective integration). This redistribution explains both individual satisfaction and collective convergence. We present a taxonomy of six metacognitive capacities organized by temporal phase, characterize their tendencies under routine AI use, and show how individually rational adaptation produces emergent social costs. The framework generates specific predictions for researchers and design principles for practitioners seeking to preserve both individual creative satisfaction and collective creative diversity.

Related papers

• Incentives shape how humans co-create with generative AI [3.182696575286433]
  We show that incentives mediate AI's homogenizing force in a creative writing task.<n>Our results reveal that the effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use.
  arXiv  Detail & Related papers  (2026-04-04T00:18:51Z)
• Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling [11.987225062711692]
  PRISM is a model-agnostic system that augments Large Language Models with dynamic On-the-fly Epistemic Graphs.<n>On three creativity benchmarks, PRISM achieves state-of-the-art novelty and significantly expands distributional diversity.<n>Results demonstrate that PRISM successfully uncovers correct long-tail diagnoses that standard LLM miss.
  arXiv  Detail & Related papers  (2026-02-24T19:38:31Z)
• Cognitive Foundations for Reasoning and Their Manifestation in LLMs [63.12951576410617]
  Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning.<n>We synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations.<n>We develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems.
  arXiv  Detail & Related papers  (2025-11-20T18:59:00Z)
• What Drives Compositional Generalization in Visual Generative Models? [56.01574461407906]
  We conduct a systematic study of how various design choices influence compositional generalization in image and video generation.<n>We identify two key factors: (i) whether the training objective operates on a discrete or continuous distribution, and (ii) to what extent conditioning provides information about the constituent concepts during training.<n>Building on these insights, we show that relaxing the MaskGIT discrete loss with an auxiliary continuous JEPA-based objective can improve compositional performance in discrete models like MaskGIT.
  arXiv  Detail & Related papers  (2025-10-03T15:02:27Z)
• DisCo: Reinforcement with Diversity Constraints for Multi-Human Generation [60.741022906593685]
  DisCo is the first RL-based framework to directly optimize identity diversity in multi-human generation.<n>DisCo fine-tunes flow-matching models via Group-Relative Policy Optimization.<n>On the DiverseHumans Testset, DisCo achieves 98.6 Unique Face Accuracy and near-perfect Global Identity Spread.
  arXiv  Detail & Related papers  (2025-10-01T19:28:51Z)
• Think Socially via Cognitive Reasoning [94.60442643943696]
  We introduce Cognitive Reasoning, a paradigm modeled on human social cognition.<n>CogFlow is a complete framework that instills this capability in LLMs.
  arXiv  Detail & Related papers  (2025-09-26T16:27:29Z)
• A Theory of Information, Variation, and Artificial Intelligence [0.0]
  A growing body of empirical work suggests that the widespread adoption of generative AI produces a significant homogenizing effect on information, creativity, and cultural production.<n>This paper argues that the very homogenization that flattens knowledge within specialized domains simultaneously renders that knowledge into consistent modules that can be recombined across them.<n>The paper concludes by outlining the cognitive and institutional scaffolds required to resolve this tension, arguing they are the decisive variable that determine whether generative AI becomes an instrument of innovation or homogenization.
  arXiv  Detail & Related papers  (2025-08-20T16:21:13Z)
• Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure [0.0]
  "Cognitive Infrastructure Studies" (CIS) is a new interdisciplinary domain to reconceptualize AI as "cognitive infrastructures"<n>CIS aims to address how AI preprocessing reshapes distributed cognition across individual, collective, and cultural scales.
  arXiv  Detail & Related papers  (2025-06-19T15:33:47Z)
• Truly Self-Improving Agents Require Intrinsic Metacognitive Learning [59.60803539959191]
  Self-improving agents aim to continuously acquire new capabilities with minimal supervision.<n>Current approaches face two key limitations: their self-improvement processes are often rigid, fail to generalize across tasks domains, and struggle to scale with increasing agent capabilities.<n>We argue that effective self-improvement requires intrinsic metacognitive learning, defined as an agent's intrinsic ability to actively evaluate, reflect on, and adapt its own learning processes.
  arXiv  Detail & Related papers  (2025-06-05T14:53:35Z)
• Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art [90.8684263806649]
  We show how AI can transcend human cognitive limitations in visual art creation. Our research hypothesizes that visual art contains a vast unexplored space of conceptual combinations. We present the Alien Recombination method to identify and generate concept combinations that lie beyond human cognitive availability.
  arXiv  Detail & Related papers  (2024-11-18T11:55:38Z)
• Emergent Collective Reproduction via Evolving Neuronal Flocks [0.0]
  This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova. VitaNova intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups.
  arXiv  Detail & Related papers  (2024-09-20T06:22:24Z)
• Can AI Be as Creative as Humans? [84.43873277557852]
  We prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. The debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data.
  arXiv  Detail & Related papers  (2024-01-03T08:49:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.